Dr Carmel Loughland

Background Childhood trauma (CT) is related to altered fractional anisotropy (FA) in individuals with schizophrenia (SZ). However, it remains unclear whether CT may influence specific cellular or extracellular compartments of FA in SZ with CT experience. We extended our previous study on FA in SZ (Costello et al., 2023) and examined the impact of CT on hypothesized lower free water-corrected FA (FAT) and higher extracellular free water (FW). Method Thirty-seven SZ and 129 healthy controls (HC) were grouped into the 'none/low' or 'high' CT group. All participants underwent diffusion-weighted magnetic resonance imaging. We performed tract-based spatial statistics to study the main effects of diagnostic group and CT, and the interaction between CT and diagnostic group across FAT and FW. Results SZ displayed lower FAT within the corpus callosum and corona radiata compared to HC (p < 0.05, Threshold-Free Cluster Enhancement (TFCE)). Independent of diagnosis, we observed lower FAT (p < 0.05, TFCE) and higher FW (p < 0.05, TFCE) in both SZ and HC with high CT levels compared to SZ and HC with none or low CT levels. Furthermore, we did not identify an interaction between CT and diagnostic group (p > 0.05, TFCE). Conclusions These novel findings suggest that the impact of CT on lower FAT may reflect cellular rather than extracellular alterations in established schizophrenia. This highlights the impact of CT on white matter microstructure, regardless of diagnostic status.

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid¿patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.

Objective: Few studies have investigated the maintenance of skills acquired in classroom-based clinician education. Using an advanced simulation-based clinical communication skill training program for postgraduate psychiatry education (ComPsych), we aimed to investigate skill acquisition through assessing changes in competence (abilities) and performance (practice). Methods: Forty trainee psychiatrists (15 males; age range 26¿48) participated. Video-recorded Standardized Patient Assessments (SPAs) were delivered twice pre- and post-training to assess learning. Skills were coded by independent psychologists using the Core Communication Skills (CCS) coding system. Simulated patients (SPs) rated trainees' communication performance using the Sim-Patient SPA checklist. Paired t-tests, linear mixed models and logistic mixed models assessed changes in communication skills over time. Results: For SPAs, reliability of coder ratings was deemed acceptable (ICC range 0.67 to 0.87). Mean post-training communication performance significantly increased for skills in agenda setting (p < 0.001), information organization (p < 0.001), empathic skills (p = 0.046), and overall skills performance (p = 0.001). Significant decreases for questioning skills were indicative of reduced reliance on these skills post-training. SPs rated all skillsets higher post-training. A modest relationship was detected between frequency (coded) and (SP-rated) quality of communication skills. Improvements in agenda setting and information organisation skills were retained ~ 6¿weeks post-training. Conclusions: Training improved patient-centered communication skills in psychiatry trainees, particularly skills in agenda setting and information organization, with skills retained ~ 6¿weeks post-training. There was reduced reliance on questioning skills, which are well utilised generally. The study supports the benefits of this method of communication skills training into postgraduate psychiatry education.

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Objective: The Corona Radiata (CR) is a large white matter tract in the brain comprising of the anterior CR (aCR), superior CR (sCR), and posterior CR (pCR), which have associations with cognition, self-regulation, and, in schizophrenia, positive symptom severity. This study tested the hypothesis that the microstructural organisation of the aCR, as measured by Fractional Anisotropy (FA) using Diffusion Tensor Imaging (DTI), would relate to poorer social cognitive outcomes and higher positive symptom severity for people with schizophrenia, when compared to healthy participants. We further hypothesised that increased positive symptoms would relate to poorer social cognitive outcomes. Methods: Data were derived from n = 178 healthy participants (41 % females; 36.11 ± 12.36 years) and 58 people with schizophrenia (30 % females; 42.4 ± 11.1 years). The Positive and Negative Symptom Severity Scale measured clinical symptom severity. Social Cognition was measured using the Reading the Mind in the Eyes Test (RMET) Total Score, as well as the Positive, Neutral, and Negative stimuli valence. The ENIGMA-DTI protocol tract-based spatial statistics (TBSS) was used. Results: There was a significant difference in FA for the CR, in individuals with schizophrenia compared to healthy participants. On stratification, both the aCR and pCR were significantly different between groups, with patients showing reduced white matter tract microstructural organisation. Significant negative correlations were observed between positive symptomatology and reduced microstructural organisation of the aCR. Performance for RMET negative valence items was significantly correlated bilaterally with the aCR, but not the sCR or pCR, and no relationship to positive symptoms was observed. Conclusions: These data highlight specific and significant microstructural white-matter differences for people with schizophrenia, which relates to positive clinical symptomology and poorer performance on social cognition stimuli. While reduced FA is associated with higher positive symptomatology in schizophrenia, this study shows the specific associated with anterior frontal white matter tracts and reduced social cognitive performance. The aCR may have a specific role to play in frontal-disconnection syndromes, psychosis, and social cognitive profile within schizophrenia, though further research requires more sensitive, specific, and detailed consideration of social cognition outcomes.

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study¿and most other large-scale human genetics studies¿was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

Schizophrenia has a heritability of 60¿80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

Objective: ComPsych communication skills training is designed to teach psychiatry trainees effective skills and strategies for undertaking key communication tasks relating to mental illness, such as schizophrenia, with patients and their families/carers. This study examined the program's feasibility, utility, and trainees' self-efficacy. Methods: Trainee cohorts attending their first year formal education course were recruited annually over 4¿years between 2015 and 2018. Each trainee attended at least one session of training in their cohort year. Trainees completed a questionnaire presenting questions about personal demographics, their perceived confidence in communication, and the effectiveness of elements of training delivery. A total of 41 trainee psychiatrists (15 male) completed the questionnaires presented at four time points (two pre-training and two post-training). Results: Participants reported a significant increase in confidence in their own communication skills post-training (d = 1.12) and rated elements of training delivery (video feedback, feedback from peers in small groups, small group facilitation, and use of simulated patients) as significantly more helpful or effective post-training (d = 0.42). Trainees also reported a significantly increased ability to critically evaluate their own communication skills post-training (d = 0.59), suggesting an increased ability to recognize their own communication skill competence. Conclusions: Following ComPsych training, trainees were more confident discussing information about schizophrenia with patients and their families/carers and were more able to critically evaluate their own communication skills: an important feature of good clinical acumen. These subjective ratings provide important self-efficacy information, including the benefits perceived and evidence of the program's feasibility and utility.

Prevention efforts can substantially reduce the human and economic costs of mental and substance use disorders. However, a strategically integrated set of priorities for research and implementation is lacking. The Leaders in Prevention Summit sought to address this need by bringing together a diverse range of stakeholders to identify shared opportunities and priorities. Eighty individual delegates were involved and 235 responses and 1338 votes were received during an interactive, online workshop. The responses were grouped thematically and ranked according to popularity. Collaboration and coordination and sustainability emerged as key themes. Identified priorities include to better understand and target risk and protective factors, share leadership and promote co-design/co-production with key stakeholders, improve coordination of data collection and management, and undertake ongoing evaluation and improvement. There was considerable agreement on the need for a coordinated national framework and strategy for prevention research, policy, and implementation and significant investment to maximise and sustain the benefits of prevention programs.

Objectives: Large-scale genetic analysis of common variation in schizophrenia has been a powerful approach to understanding this complex but highly heritable psychotic disorder. To further investigate loci, genes and pathways associated more specifically in the well-characterized Australian Schizophrenia Research Bank cohort, we applied genome-wide single-nucleotide polymorphism analysis in these three annotation categories. Methods: We performed a case¿control genome-wide association study in 429 schizophrenia samples and 255 controls. Post-genome-wide association study analyses were then integrated with genomic annotations to explore the enrichment of variation at the gene and pathway level. We also examine candidate single-nucleotide polymorphisms with potential function within expression quantitative trait loci and investigate overall enrichment of variation within tissue-specific functional regulatory domains of the genome. Results: The strongest finding (p = 2.01 × 10-6, odds ratio = 1.82, 95% confidence interval = [1.42, 2.33]) in genome-wide association study was with rs10252923 at 7q21.13, downstream of FZD1 (frizzled class receptor 1). While this did not stand alone after correction, the involvement of FZD1 was supported by gene-based analysis, which exceeded the threshold for genome-wide significance (p = 2.78 × 10-6). Conclusion: The identification of FZD1, as an independent association signal at the gene level, supports the hypothesis that the Wnt signalling pathway is altered in the pathogenesis of schizophrenia and may be an important target for therapeutic development.

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Genome-wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re-analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity-by-descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow-up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.

Background: There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr). Results: We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power. Conclusions: With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.

Background: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. Methods: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11¿78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10¿87 years; 53% male) assessed with standardized methods at 39 centers worldwide. Results: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. Conclusions: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ~150,000 individuals give a higher accuracy than LDSC estimates based on ~400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

White matter abnormalities associated with schizophrenia have been widely reported, although the consistency of findings across studies is moderate. In this study, neuroimaging was used to investigate white matter pathology and its impact on whole-brain white matter connectivity in one of the largest samples of patients with schizophrenia. Fractional anisotropy (FA) and mean diffusivity (MD) were compared between patients with schizophrenia or schizoaffective disorder (n = 326) and age-matched healthy controls (n = 197). Between-group differences in FA and MD were assessed using voxel-based analysis and permutation testing. Automated whole-brain white matter fiber tracking and the network-based statistic were used to characterize the impact of white matter pathology on the connectome and its rich club. Significant reductions in FA associated with schizophrenia were widespread, encompassing more than 40% (234ml) of cerebral white matter by volume and involving all cerebral lobes. Significant increases in MD were also widespread and distributed similarly. The corpus callosum, cingulum, and thalamic radiations exhibited the most extensive pathology according to effect size. More than 50% of cortico-cortical and cortico-subcortical white matter fiber bundles comprising the connectome were disrupted in schizophrenia. Connections between hub regions comprising the rich club were disproportionately affected. Pathology did not differ between patients with schizophrenia and schizoaffective disorder and was not mediated by medication. In conclusion, although connectivity between cerebral hubs is most extensively disturbed in schizophrenia, white matter pathology is widespread, affecting all cerebral lobes and the cerebellum, leading to disruptions in the majority of the brain's fiber bundles.

Objective: This research sought to gain insight into the processes used by clinicians to discuss a schizophrenia diagnosis with patients/families, with the aim of informing the development of a communications skills training program. Methods: A generic qualitative methodological approach was used. Sixteen mental health clinicians were recruited. Semi-structured individual interviews were used to explore their perceptions and experiences communicating a schizophrenia diagnosis. Interviews were recorded, transcribed, and thematic analysis undertaken. Results: There were five key themes relating to the process of communication about a diagnosis of schizophrenia: (1) orientation to patient care, (2) planning of communication, (3) the impact of team leadership and inter/intra-professional functioning on communication tasks, (4) the roles of different clinicians in communicating about diagnosis and treatment, and (5) time and resource deficiencies. Despite expressing care and concern for vulnerable patients and embracing the concept of multidisciplinary teams, communicating diagnostic information to patients and families was generally unplanned for, with little consistency regarding leadership approaches, or how the team communicated diagnostic information to the patient and family. This contributed to tensions between different team members. Conclusion: The findings demonstrated a number of issues compromising good communication around a schizophrenia diagnosis, both in terms of clinician skill and clinical context, and support the importance of education and training for all members of the multidisciplinary team about their role in the communication process.

Objective: Important gaps are observed in clinicians' communication with patients and families about psychiatric disorders such as schizophrenia. Communication skills can be taught, and models for education in these skills have been developed in other fields of medicine, such as oncology, providing a framework for training communication skills relevant to psychiatric practice. This study evaluated a pilot communication skills education program for psychiatry trainees, focusing on discussing schizophrenia diagnosis and prognosis. Method: Communication skills training modules were developed based on an existing theoretical framework (ComSkil), adapted for discussing a schizophrenia diagnosis and prognosis. Pre-post training rating of self-reported confidence in a range of communication tasks was obtained, along with trainee views on the training methods. Results: Thirty-eight participants completed the training. Significant improvements in confidence were reported post training for discussing schizophrenia prognosis, including an increased capacity to critically evaluate their own communication skills. Participants reported high levels of satisfaction with the program. Conclusion: This preliminary study provides support for the translation of a well-established educational model to psychiatric training addressing core clinical communication tasks and provides the foundation for the development of a more comprehensive evaluation and an extended curriculum regarding other aspects of care for patients with schizophrenia: ongoing management and recovery, dealing with conflict, and conducting a family interview.

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia. © 2014 Macmillan Publishers Limited. All rights reserved.

Background: 22q11.2 deletion syndrome (22q11DS) is associated with a number of physical anomalies and neuropsychological deficits including impairments in executive and sensorimotor function. It is estimated that 25% of children with 22q11DS will develop schizophrenia and other psychotic disorders later in life. Evidence of genetic transmission of information processing deficits in schizophrenia suggests performance in 22q11DS individuals will enhance understanding of the neurobiological and genetic substrates associated with information processing. In this report, we examine information processing in 22q11DS using measures of startle eyeblink modification and antisaccade inhibition to explore similarities with schizophrenia and associations with neurocognitive performance. Methods: Startle modification (passive and active tasks; 120- and 480-ms pre-pulse intervals) and antisaccade inhibition were measured in 25 individuals with genetically confirmed 22q11DS and 30 healthy control subjects. Results: Individuals with 22q1 1DS exhibited increased antisaccade error as well as some evidence (trend-level effect) of impaired sensorimotor gating during the active condition, suggesting a dysfunction in controlled attentional processing, rather than a pre-attentive dysfunction using this paradigm. Conclusions: The findings from the present study show similarities with previous studies in clinical populations associated with 22q11DS such as schizophrenia that may indicate shared dysfunction of inhibition pathways in these groups.

Background Schizophrenia is associated with lower pre-morbid intelligence (IQ) in addition to (pre-morbid) cognitive decline. Both schizophrenia and IQ are highly heritable traits. Therefore, we hypothesized that genetic variants associated with schizophrenia, including copy number variants (CNVs) and a polygenic schizophrenia (risk) score (PSS), may influence intelligence. Method IQ was estimated with the Wechsler Adult Intelligence Scale (WAIS). CNVs were determined from single nucleotide polymorphism (SNP) data using the QuantiSNP and PennCNV algorithms. For the PSS, odds ratios for genome-wide SNP data were calculated in a sample collected by the Psychiatric Genome-Wide Association Study (GWAS) Consortium (8690 schizophrenia patients and 11 831 controls). These were used to calculate individual PSSs in our independent sample of 350 schizophrenia patients and 322 healthy controls. Results Although significantly more genes were disrupted by deletions in schizophrenia patients compared to controls (p = 0.009), there was no effect of CNV measures on IQ. The PSS was associated with disease status (R 2 = 0.055, p = 2.1 × 10 -7) and with IQ in the entire sample (R 2 = 0.018, p = 0.0008) but the effect on IQ disappeared after correction for disease status. Conclusions Our data suggest that rare and common schizophrenia-associated variants do not explain the variation in IQ in healthy subjects or in schizophrenia patients. Thus, reductions in IQ in schizophrenia patients may be secondary to other processes related to schizophrenia risk. © Cambridge University Press 2013.